The present invention relates to the field of image processing, and more particularly to a method and system for assessing the quality of spectral images.
It has recently become possible to commercially obtain satellite and aerial images of terrain of interest from a number of sources. For example, certain large farms currently use satellite images provided by Landsat, the system of land-observing satellites operated by the federal government. Landsat satellites orbit the earth at approximately 900 km., and provide images in which each pixel represents a square area of between 1 m2 and 1E6 m2 (a pixel area of 100 m2 is common for systems designed for land-use purposes). Visible, near-infrared, shortwave infrared, thermal infrared sensors deployed on such satellites can detect, among other things, the spectral reflectance, temperature, and other physical characteristics of specified terrestrial areas. In one application, these images are overlaid onto farm mapping programs to show areas of plant stress or potential yield.
The sensors used in generating the images used for many commercial purposes are typically characterized as either xe2x80x9cmultispectralxe2x80x9d or xe2x80x9chyperspectralxe2x80x9d. Multispectral sensors collect images of a terrain or landscape and provide a handful of wide spectral bands of imagery. These bands encompass the visible, short wave infrared, and, in some cases, thermal infrared portion of the electromagnetic spectrum. Similarly, hyperspectral sensors typically provide hundreds of narrow spectral bands of spatial imagery spanning the visible, near-infrared, and shortwave infrared portion of the electromagnetic spectrum. As a result, images obtained using hyperspectral sensors generally afford greater spectral discrimination than those obtained using multispectral sensors.
Despite the existence of myriad techniques for processing image data collected from multispectral and hyperspectral sensors, there is not known to exist an objective standard for determining the quality of an image based upon its spectral characteristics. Conventionally, image quality is inferred based upon measurements of a number of parameters including, for example, spatial resolution, calibration accuracy, spectral resolution, signal to noise, contrast, bit error rate, dynamic range, sensor stability, and geometric registration. A manual and subjective image quality evaluation is known as the Multispectral Imagery Interpretability Rating Scale (xe2x80x9cMS IIRSxe2x80x9d). However, the MS IIRS is currently continuing to be refined, and is not widely used. Attempts have also been made to derive mathematical constructs indicative of image quality. One such construct, known as The General Image Quality Equation (xe2x80x9cGIQExe2x80x9d), is used in parametric evaluation of single band images. See, e.g., Leachtenauer, J. C., Malila, W., Irvine J., Colburn, L., and Salvaggio, N., Nov. 10, 1997, General Image-Quality Equation, Applied Optics, Vol. 36, No. 32. The GIQE may also be used to produce an image quality value applicable to the National Interpretability Rating Scale (xe2x80x9cNIIRSxe2x80x9d). However, the MS IIRS, GIQE and NIIRS are not known to be useful in objectively assessing the quality of multispectral or hyperspectral images.
In summary, the present invention pertains to a method for evaluating quality of an image. The inventive method contemplates receiving a spectral image and extracting a plurality of pixels therefrom. The plurality of pixels are converted into a plurality of spectral vectors, wherein each element in each spectral vector represents a property of a respective one of N spectral bands. The plurality of spectral vectors are then categorized into a set of M classes. The inventive method further includes the step of computing a mean vector for each of the M classes based upon the spectral vectors associated therewith. Next, spectral similarity values between pairs of the mean vectors are computed. The distribution of these spectral similarity values may then be analyzed in order to obtain information relevant to image quality.
In another aspect, the present invention relates to a method for evaluating quality of a received spectral image. The pixels from the spectral image are first organized into a plurality of classes. A mean spectral vector is then computed for each of the plurality of classes. The inventive method further includes the step of computing spectral similarities between pairs of the mean spectral vectors. These spectral similarities are then analyzed in order to obtain information relevant to quality of the received spectral image.
The present invention also pertains to an image processing system having an input interface through which is received a spectral image. The image processing system further includes a storage medium having stored therein an image quality assessment stored program. A processor is operative to execute the image quality assessment stored program and thereby: (i) organize pixels from the spectral image into a plurality of classes, (ii) determine a mean spectral vector for each of said plurality of classes, (iii) compute spectral similarities between pairs of said mean spectral vectors, and (iv) analyze said spectral similarities in order to obtain information relevant to quality of the image.
In another aspect, the present invention relates to an image processing system having an a input interface through which is received a spectral image. The image processing system further includes a storage medium having stored therein an image quality assessment stored program. A processor is operative to execute the image quality assessment stored program and thereby: (i) extract a plurality of input pixels from the spectral image, (ii) convert the plurality of input pixels into a plurality of spectral vectors, each element in each of the spectral vectors representing a reflectance of a respective one of a plurality of spectral bands, (iii) organize said plurality of spectral vectors into a set of M classes, (iv) determine a mean reflectance vector associated with each of the M classes, (v) compute spectral similarities between pairs of these mean reflectance vectors, and (iv) analyze these spectral similarities in order to obtain information relevant to quality of the received spectral image.